In 2023, the industry experienced a significant disruption with the emergence of conceptual artificial intelligence, which has now become a pivotal element in 2024 initiatives.
To support the advancement of gen AI, specialized professionals need to focus on enhancing and upgrading their data management platforms to drive improved business outcomes. Gen AI relies on information that is AI-ready.
However, the development process often leads to intricate and complex rollout scenarios due to the enterprise-wide implications of general AI. Technical experts are now tasked with seamlessly integrating data management and architectures across various environments, including on-premises, cross, edge, and fog systems.
The velocity, diversity, and volume of data generated by modern applications and scientific workloads may overwhelm traditional solutions that technical professionals have historically relied on. As a result, they must adopt innovative approaches to data storage and delivery to cater to the requirements of gen AI. This shift may impact data ecosystems such as data lakes, warehouses, and hubs.
With the rise of gen AI, technical professionals have the opportunity to elevate performance and efficiency in data management practices. To achieve this and enhance their data architecture in alignment with general AI principles, professionals should consider the following four key trends:
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Emphasizing Strong Design and Integration for Flexible Data Architecture:
- Establishing a plan and communities of practice to address the information environment.
- Prioritizing deployments based on connectivity, capacity, and data volume to support conceptual AI capabilities.
- Enhancing the information management architecture to facilitate gen AI capabilities.
- Supporting diverse architectural patterns to develop robust data infrastructures.
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Anticipating Long-term Impacts of Data Backup Decisions:
- Harmonizing the architecture of data lakes and data warehouses to boost productivity and efficiency.
- Outlining use cases for data backup options using Gartner’s choice framework.
- Understanding the complexities of deploying distributed databases.
- Managing complex information management systems and linking data warehouses effectively.
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Driving Data Integration Pipelines with Innovative Data Architecture:
- Addressing data quality issues in data pipelines.
- Orchestrating processes and data network across multiple deployments.
- Establishing an infrastructure for data integration in multicloud environments.
- Adopting file observability practices to ensure reliable information management.
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Implementing Comprehensive Rollout Procedures:
- Embracing open standards and open source for future-proof assets.
- Enhancing cloud deployment with cloud financial management for cost optimization.
- Exercising caution and monitoring the performance of gen AI implementations closely.
To prepare their data infrastructure for the long-term utilization of generative AI, technical professionals should focus on these evolving trends. By evaluating these strategies holistically, they can effectively navigate the complexities and opportunities presented by general AI.
Ramke Ramakrishnan, a top director analyst at Gartner Inc., brings extensive expertise in guiding data administration strategies and fostering excellence through innovation and technology professional agility. This content was authored by him for SiliconANGLE. For further insights on emerging technologies, including general AI, attend the Gartner Data & Analytics Summit scheduled from March 11 to 13 in Orlando, Florida.
